ultra-large chemical libraries Archives - Pharmacelera | Pushing the limits of computational chemistry

How can we screen 31 billion compounds? Divide and conquer!

Fernando Martín — Tue, 30 May 2023 13:28:37 +0000

Enric Herrero

May 30th, 2023

Commercial chemical libraries have witnessed remarkable growth in recent years, resulting in an unprecedented increase in size and diversity. With advancements in high-throughput synthesis and combinatorial chemistry techniques, compound providers like Enamine have expanded their collections of small organic molecules to meet the escalating demands of the pharmaceutical industry. This exponential growth has provided researchers worldwide with access to an extraordinary wealth of chemical diversity, facilitating the discovery and development of novel therapeutic agents.

However, the significant growth in size and diversity of commercial chemical libraries has rendered previous methods of virtual screening impractical, especially for accurate 3D methods. The sheer volume of compounds amassed within these libraries presents immense challenges in terms of storage and computational costs. Taking as a reference a compressed SD file containing multiple stereoisomers and conformers of a molecule of 48KB, the fully enumerated library of Enamine REAL (31 billion compounds) would require a storage capacity of 1.36 PB of data! This is almost 1400 hard drives like the one that you have in your laptop! Following the same example, if we assume that processing this single molecule requires 3.6 ms in your laptop, this will mean 3.5 years of calculations to perform a screening!

Luckily, several methods have been proposed that rely on the way these huge libraries are created, which is combining a set of building blocks to generate new molecules. Figure 1 shows an example of the value of using building blocks to perform a screening, for simplicity we will assume that each building block is a synthon and that all of them can be combined. In this example we have a building block library of 3 building blocks that can generate a chemical space of 9 molecules (combining all against all). If we apply the traditional approach (Brute force) we would compare our reference structure against each of the molecules of the library, this is 9 comparisons. However, if we perform a smart enumeration taking advantage of how this library has been created, we can reduce the computing cost. In this case what we would do is to partition the reference structure in two fragments and instead of comparing against all the enumerated library we perform the comparison against the building block library, this is 3 comparisons. Since we have two reference fragments we need to perform this operation twice, resulting in 6 comparisons, 3 less than in the brute force approach. This difference in the number of comparisons does not seem large but if we translate this example to a building block library of 100K building blocks, in the brute force approach we would need 10 million comparisons vs 200 thousand for the smart enumeration approach.

If we plot the computational cost projections of both methods for different library sizes (Figure 2) we can see how the scalability of the smart enumeration approach is much better than the brute force approach and, therefore, is much more suitable for the chemical libraries of the future.

Overall, we have seen that the rapid growth of commercial chemical libraries represents a challenge for virtual screening tools. The size and diversity of these libraries have made traditional screening methods impractical due to storage and computational costs. However, the use of smart enumeration based on building blocks offers a more efficient approach. By leveraging the way these libraries are created, researchers can significantly reduce the number of comparisons needed for screening. This smart enumeration approach shows better scalability and is considered more suitable for future chemical libraries, offering computational efficiency compared to brute force methods.

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Exploring an ultra-large chemical space

Fernando Martín — Wed, 03 May 2023 12:37:51 +0000

Fernando Martin

May 4th, 2023

An exponential growth of the accessible chemical space

In the last years, there has been an exponential growth in commercial chemical libraries from millions to billions. To put some numbers: ZINC database has increased its size 37.000-fold since its 2012 version and Enamine REAL size is now over the 36 billion of compounds. Traditional computational chemistry approaches might not be usable anymore with these libraries due to computational and timing costs.

Enric Herrero, CTO at Pharmacelera, had the chance to talk about this topic at the Boston Area Group for Informatics and Modeling (BAGIM) last march. The presentation pointed out the need for novel tools that help exploring this novel ultra-large available chemical space and why current methods struggle to deal with it. During the presentation, different methods were listed, focusing in alternatives for ligand-based methods: brute-force (full enumeration), sampling methods and combinatorial search. Special emphasis was applied to the two latest, since they offer an alternative to screen larger chemical spaces than brute force using fewer computing resources. In this post we will quickly introduce approaches: MolPAL, based on sampling methods; and combinatorial search using 3D hydrophobic descriptors.

Sampling virtual screening powered by AI: MolPAL

This sampling method, based on David E. Graff paper, establishes that only a small subset (around 2.4%) of a full library must be evaluated with a slow screening method (i.e. docking or 3D ligand-based similarity) to obtain the same results than a brute-force method.

This is done in an iterative way, sampling first a random subset (~0.4%), evaluating the score with the slow method and then using the results to train a machine learning (ML) model. This model would be later applied to predict the score of all the ligands in the full library and select a new subset to be evaluated with the slow method.

To evaluate the capabilities of the method, our team has applied PharmScreen as scoring method to train a machine learning model. One of the main advantages observed when comparing MolPAL with brute-force screening (here, run PharmScreen for a full library), is the reduction in terms library storage (1B of compounds will suppose 44TB with brute force while MolPAL method will require only 96MB) as well as the computing speed.

An important aspect of sampling methods is that their performance will be linked to the speed ratio of the method used to mine the library vs the ML model training and prediction: the slowest or more computationally intensive the method is with respect to the ML training and prediction, the better in terms of speed gain.

Combinatorial search

Combinatorial search methods take advantage of the combinatorial chemistry concept, where the chemical space is explored using building block libraries and reaction information. By partitioning a reference molecule in fragments, screening software tools based on this method can explore libraries to find similar build blocks and enumerate only those compounds that are more similar. Since the reactivity of these building blocks is considered, one can easily reconstruct novel and synthesizable compounds that can be easily tested in the laboratory.

These methods can also take advantage of 3D information and the derived physicochemical properties when assessing the building block similarity. We have observed how the application of 3D hydrophobic molecular descriptors can help finding more diverse compounds with similar physicochemical properties than 2D methods.

Combinatorial search methods provide the best scalability among all the evaluated methods and, therefore, are a good alternative for the screening of multi-billion sized libraries. To put an example, using 3D methods as mentioned above and a library of 137K building blocks, we can explore a potential space of 31B of synthesizable molecules.

Conclusions

The exponential growth of the accessible chemical space is driving the generation of new methods that will help screening it. Contrary to full enumeration methods, that explore large libraries in exchange of using more computational resources, these new approaches can explore huger chemical libraries with more feasible hardware configurations.

Sampling methods, such as MolPAL, represent a good choice when screening large libraries using computing-demanding methods, such as docking or 3D ligand-based similarity. These methods are also interesting when storage capabilities suppose a problem.

Similarly, combinatorial search methods are a smart solution when screening ultra-large libraries, such as Enamine REAL. The use of building block libraries while considering their reactivity maximizes the synthesizability of novel compounds.

Pharmacelera is focused on offering novel solutions to explore this ultra-large chemical space. If you want to learn more about it, contact our team. They will inform you about our new services in this field and new technologies to come.

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Pharmacelera to partner with Enamine for the screening of ultra-large chemical libraries

Fernando Martín — Thu, 15 Dec 2022 09:28:44 +0000

Barcelona, Spain, and Kyiv, Ukraine, 15 December 2022. Pharmacelera, the leading provider of computational tools for hit discovery, and Enamine, the developer of REAL Database – the world’s largest virtual library of highly feasible compounds, have announced a partnership to jointly provide an efficient solution for hit finding. Ultra-large chemical libraries are observed as one of the key paradigms to access an unexplored chemical space. How to traverse these enormous spaces accurately is a research area that has raised the interest of the pharmaceutical industry, since finding novel hits with chemical diversity is a fundamental pillar in drug discovery.

In this context, Pharmacelera and Enamine have reached an agreement to plug the Enamine Real Database – over 5.5 billion highly feasible compounds – into the new version of Pharmacelera’s virtual screening flagship tool PharmScreen®. The resulting joint solution will allow the accurate screening of the ultra-large chemical library, providing its users not only with access to the physical compounds for testing from Enamine but also with new Intellectual Property (IP) for their targets of interest.

“Pharmacelera offers an original, scientifically sound, and meaningful way of reading and interpreting the molecules, with excellent further applicability for virtual screening in Drug Discovery programs. We are happy to partner with Pharmacelera, to enable a more straightforward connection of PharmScreen® to the real test compounds”, said Michael Bossert, Head of Strategic Alliances at Enamine.

“This agreement is extremely aligned with Pharmacelera’s strategy to work with leading institutions in the fields of Drug Discovery that have complementary technology and expertise”, says Rémy Hoffmann, Chief Business Development Officer at Pharmacelera. “We are thrilled to start this collaboration with Enamine, the prominent compound provider, as it will allow us to apply our accurate Quantum-Mechanics (QM) and Machine Learning (ML) algorithms to mine the Enamine’s REAL Database”, said Enric Gibert, Pharmacelera’s CEO.

ENDS

About Enamine

Enamine is a global leading designer and the largest producer of building blocks (280,000+ compounds in stock) and screening libraries (3M+ compounds in stock). REAL® Database is a collection of currently 5.5 billion enumerated compounds that can be synthesized within only 3 weeks with more than 80% success rate. This database is complemented with REAL® Space providing access to over 32 billion REAL Compounds through the compilation of 137,000 building blocks used in 167 different synthesis protocols. Enamine offers integrated drug discovery services with seamless and efficient hit follow-up support.

About Pharmacelera

Pharmacelera develops advanced computational tools for the discovery of novel hits using accurate Quantum-Mechanics (QM), Artificial Intelligence (AI), and High-Performance Computing (HPC). The company’s products PharmScreen® and PharmQSAR use 3D molecular descriptors derived from Quantum-Mechanics (QM) calculations to mine an unexplored chemical space and to identify hits uncovered by traditional algorithms. Pharmacelera is a private company founded in 2015 and based in Barcelona, Spain. The company works with several big pharma and biotech organizations across Europe and the United States.

Media Contacts

Pharmacelera

Rémy Hoffmann, CBDO (remy.hoffmann@pharmacelera.com)

Enamine Ltd.

Michael Bossert, Head of Strategic Alliances (m.bossert@enamine.net)

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